Spatial Data Analysis (450:320)

Class 3

The code behind the title page map:

lapply(c("sf", "terra", "ggplot2", "rasterVis", "gstat"), 
       library, character.only = TRUE)

# 1. Create a 50 * 50 template SpatRaster
xy_rast <- rast(
    ncols = 50, nrows = 50, xmin = 0.5, xmax = 50.5, ymin = 0.5, ymax = 50.5)

# 2. Define the gstat object and interpolate to the raster
set.seed(2)
gdummy <- gstat(
    formula = z ~ 1, locations = ~x + y, dummy = TRUE, beta = 1,
    model = vgm(psill = 0.35, range = 30, model = 'Sph'), nmax = 20)
r <- interpolate(xy_rast, gdummy, nsim = 1)

# Plot
levelplot(
    r, scales = list(draw = FALSE), axes = FALSE,  
    colorkey = list(axis.line = list(col = "#FAF0E6"), 
                    axis.text = list(col = "#FAF0E6")))

Class overview

Fundamentals of R (data types and objects, and basic operations)
R packages
More on Git/GitHub
Overview of assignment
Trouble shooting

A few coding tips

Use <- for assignment and = for passing arguments to functions.
- This is the standard convention in R, and I expect you to follow it.
- You may encounter R code that uses = for assignment. That is acceptable to read and understand.
Shortcut for <-
- MacOS: option + -
- Windows: Alt + -

A few coding tips

In R Markdown, shortcut to insert an R code chunk
- Windows: Ctrl + Alt + I
- MacOS: option + command + I
Shortcut to run selected lines
- Windows: Ctrl + Enter
- MacOS: command + Enter
Help
- ?function
- ??function

Fundamentals of R

Q: Why do we need to learn the fundamentals of R for working with spatial data? Hint:

What are the two main data types in GIS?
What do they look like?

Fundamentals of R

Data types

There are 6 atomic data types (or we could say classes of objects):

character, e.g. 'hello world', 'abc'
double (real or decimal), e.g. 10, 3.14, 1e10
integer, e.g. 1L
logical, e.g. TRUE, FALSE, T, F
complex, e.g. 1 + 3i (not commonly used by us)
raw (rarely used by us)

Fundamentals of R

Other special data types

NULL: means not exist
Missing data: NA. It is a special logical type, but can be convert to the specific type automatically when it is used with other data types.
Infinity: Inf (e.g. 1 / 0) and -Inf (e.g. -1 / 0. They are special double type.
Undefined value: NaN. It is also a special double type. (e.g. 0 / 0)

Fundamentals of R

Other special data types

Date: as.Date('1970-1-5')
Time: as.POSIXct('1970-1-5')
Factor: factor could be treated as integers with labels for categorical data. Function levels(f) can get these labels.

Fundamentals of R

Data type checking

typeof(x)
is.xxx(x):
- e.g. is.double(x), is.integer(x), is.logical(x), is.character(x), is.complex(x), is.raw(x), is.factor
- is.numeric(x) exams that objects behave like numbers.
- e.g. is.na(y), is.nan(y), is.null(y), is.infinite(y), is.finite(y).

Fundamentals of R

Data type converting

as.xxx(x):
- e.g. as.numeric(x), as.double(x), as.character(x), as.integer(x), as.logical(x), as.complex(x), as.raw(x), as.factor(x), as.Date(x), as.POSIXct(x).
logical < integer < double < character

Fundamentals of R

Vectors

Atomic vectors (usually refer to vector):
- A vector is a sequence of objects of the same class.
- Arrays and matrices are special type of vectors with dimensions.
  - Matrices have 2 dimensions.
  - Arrays could have higher dimensions.

Fundamentals of R

Vectors

Lists
- Superficially for users, a list can contain objects of different classes.
- Like vectors, a list can also be converted to list-matrix or list-array by defining dimensions.
- data.frame and tibble are two S3 object-oriented objects that are created upon list for tabular data.

Fundamentals of R

Vectors

Fundamentals of R

Creation

Function vector(mode, length) can be used to initialize a vector of specific data type with defined length.
More commonly, function c (short for concatenate) can be used to create vectors.
cbind and rbind are functions can bind vectors to a matrix. Function matrix(data_vector, nrow, ncol) also can be used to create a matrix, and array(data_vector, dim) for array.
Anything can feed in function list to create a list, the same as data.frame.
is.xxx(x) and as.xxx(x) also works for vector, matrix, array, list, and data.frame.

Fundamentals of R

Atrributes

Any objects have attributes. If nothing is set, the attributes of an object is NULL. Some common attributes include:

names/dimnames/rownames/colnames
Overall length, and more specifically dim for matrix and array. Resetting dim attribute can convert a vector to matrix/array or reconstruct a matrix/array.

I’ll leave you with a short homework to review what we’ve learned.
Now, let’s get back to your package.

Installing and using packages

http://r-pkgs.had.co.nz/package.html

Package structure

General common parts of a package.

Package structure

You main tasks for the assignment package are update:

DESCIPTION file to incorporate updated info (e.g. your package version)
.R files in the R/ folder (e.g. write functions)
.Rmd files in the vignettes/ folder (main files for assignments)

Add a function to your package

Why we need functions and packages?
- Reuse -> Reproducibility
Add a my_number_checker function to your package (let me show you first)
Add a vignette to introduce the function
- Use usethis::use_vignette("vignette_name")
Update and install the package locally.
Git add and commit all updates.

Git branching

Create a new branch named test and push to the remote repo
- git branch test (create a new branch)
- git checkout test (switch to the new branch)
- git push origin test (push the new branch to GitHub)
Have a look your remote repo on GitHub

Install your package from GitHub

In R Console, run

devtools::install_github("your repo", build_vignettes = TRUE, 
                     auth_token = "paste_your_github_token_here")

Our Branching Model for assignments

Delete a branch

Delete the branch test locally and remotely
- git checkout main (park to another branch)
- git branch -d test (delete locally)
- git push origin --delete test (delete remotely)

Homework

Write an R markdown that summarizes what we learned about the fundamentals of R.
- Ideally in your class note folder for your own records
- The knitted html should look like this: Expected output.
Post your html file in our Google Chat.
- This counts for your participation.
Read the rest of Unit1-Module1 and Unit1-Module 2 (we have already covered most of the material).
Assignment 1 is due this Friday!
- It is at the end of Unit1-Module1
- Don’t worry! After today’s class, it’ll be an easy task.